Learning classes of efficient codes

Te-Won Lee; Michael S. Lewicki

doi:10.1117/12.408633

4 December 2000 Learning classes of efficient codes

Te-Won Lee, Michael S. Lewicki

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408633
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

We are interested in leaning efficient codes to represent classes of different images. The image classes are modeled using an ICA mixture model that assumes that the data was generated by several mutually exclusive data classes whose components are a mixture of non-Gaussian sources. The parameters of the model can be adapted using an approximate expectation maximization approach to maximize the data likelihood. We demonstrate that this method can learn classes of efficient codes to represent images that contain a variety of different structures. The learned codes can be used for image compression, de-noising and classification tasks. Compared to standard image coding methods, the ICA mixture model gives better encoding results because the codes are adapted to the structure of the data.

Citation Download Citation

Te-Won Lee and Michael S. Lewicki "Learning classes of efficient codes", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408633

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KEYWORDS

Independent component analysis

Data modeling

Image segmentation

Image classification

Image processing

Image compression

Image processing algorithms and systems

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